Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation

Automatic detection of retinal blood vessels and measurement of vessel diameter are important steps in the computer aided diagnosis in ophthalmology. Here, we present a new multi-scale vessel enhancement method based on complex continuous wavelet transform (CCWT). The parameters of CCWT are optimized to represent line structures in all directions and separate them from simple edges. The final vessel network is obtained by applying an adaptive histogram-based thresholding process along with a proper length filtering method. An efficient circular structure operator is employed on the centerline of vessels to estimate their diameters. The performance of the proposed method is measured on the publicly available DRIVE and STARE databases and compared with several state-of-the-art methods as well as second observer. The proposed method shows much higher accuracy (95%) and sensitivity (79%) in the same range of specificity (97%). The predictive value of it is higher than 72.9%. The vessel diameter estimation process also shows lower root mean square error compared to the existing methods and second observer.

► This paper presents a new multi-scale vessel enhancement method based on complex continuous wavelet transform.
► The final vessel network is obtained by applying an adaptive histogram-based thresholding process which separate line structures from simple edges.
► An efficient circular structure operator is employed on the centerline of vessels to estimate their diameters.
► The proposed method shows much higher sensitivity (79%) in the same range of specificity (97%) compared with several state-of-the-art methods as well as second observer.